The present invention generally relates to parallel sensing and processing of signals and, more particularly, to a signal phase pattern sensitive neural network system and method for discerning persistent patterns of phase in time varying excitation.
An artificial neural network is a network of highly interconnected processing elements (PE's) which accomplish parallel processing and, to some extent, distributed processing. The neural network can be coupled with an array of sensors to form an artificial neural system for sensing and processing signals wherein learning and adapting occur in a way thought to simulate the human brain.
A typical neural network system is composed of multiple layers. In a layer of the neural network system where learning occurs, the layer is characterized by the fact that all the member PE's of the layer have the same learning rule. For example, consider the conventional neural network system 10 illustrated in FIG. 1. First layer 12 merely acts as a "fan-out" layer, connecting each sensor 14 with each member PE of second layer 16. No competition nor learning occurs in first layer 12.
The PEs of second layer 16 compete with one another. By way of example, competition between second layer PEs can occur in accordance with a learning rule that is known as the Kohonen competitive learning rule and is set out in detail in the text Self-Organization and Associative Memory by T. Kohonen, Springer-Yerlag, Germany, 1984. The Kohonen competitive learning rule provides for competition between PEs of second layer 16 on the basis of the equation: ##EQU1## The winner among PEs updates itself (or adjusts its weight) in accordance with the equation: EQU W.sub.i =.alpha.(x-w.sub.i)z.sub.i
where x is typically a signal vector, W is a connectivity strength or weight vector which is modified during learning by the quantity W.sub.i for the ith PE, and alpha is a heuristically chosen learning constant.
Although each PE of second layer 16 operates locally upon the inputs it receives, only the winning PE, i.e., the one whose connection strength pattern most closely matches the incoming signals, is allowed to adapt. The connection strength patterns associated with the PEs form win regions or sectors for each PE in the manner of Kohonen competitive learning.
Heretofore, neural networks have been applied to preprocessed signals which are expressed as intensities or base-banded amplitude envelopes. However, there is growing interest in applying neural network technology to real world signals, for instance, to improve performance of weapon sensory systems (passive and active) against low Doppler targets, targets employing countermeasures, and targets in areas of high environmental noise.